'''simple docstring'''




from typing import Callable, List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


__UpperCAmelCase					=logging.get_logger(__name__)  # pylint: disable=invalid-name




class        a__ (  UpperCAmelCase__				,  UpperCAmelCase__					):






     @register_to_config
     def __init__(					self						:						Optional[int]    ,							a						:						bool    ,							a						:						Optional[int] = None    ,							a						:						Optional[int] = None       ):





         """simple docstring"""



         super().__init__()

         __lowerCamelCase            =    learnable

         if self.learnable:
             assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
             assert length is not None, "learnable=True requires `length` to be set"

             __lowerCamelCase            =    torch.zeros(a    ,							a       )
         else:
             __lowerCamelCase            =    None

         __lowerCamelCase            =    torch.nn.Parameter(a       )




class        a__ (  UpperCAmelCase__					):
     lowerCamelCase    :	VQModel
     lowerCamelCase    :	CLIPTextModel
     lowerCamelCase    :	CLIPTokenizer
     lowerCamelCase    :	TransformeraDModel
     lowerCamelCase    :	LearnedClassifierFreeSamplingEmbeddings
     lowerCamelCase    :	VQDiffusionScheduler






     def __init__(					self						:						List[str]    ,							a						:						VQModel    ,							a						:						CLIPTextModel    ,							a						:						CLIPTokenizer    ,							a						:						TransformeraDModel    ,							a						:						VQDiffusionScheduler    ,							a						:						LearnedClassifierFreeSamplingEmbeddings    ,							):





         """simple docstring"""



         super().__init__()

         self.register_modules(
             vqvae=a    ,							transformer=a    ,							text_encoder=a    ,							tokenizer=a    ,							scheduler=a    ,							learned_classifier_free_sampling_embeddings=a    ,							)






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[Any]    ,							a						:						Dict    ,							a						:						List[Any]    ,							a						:						Optional[Any]       ):





         """simple docstring"""



         __lowerCamelCase            =    len(a       ) if isinstance(a    ,							a       ) else 1

         # get prompt text embeddings
         __lowerCamelCase            =    self.tokenizer(
             a    ,							padding='''max_length'''    ,							max_length=self.tokenizer.model_max_length    ,							return_tensors='''pt'''    ,							)
         __lowerCamelCase            =    text_inputs.input_ids

         if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
             __lowerCamelCase            =    self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]       )
             logger.warning(
                 '''The following part of your input was truncated because CLIP can only handle sequences up to'''
                 f""" {self.tokenizer.model_max_length} tokens: {removed_text}"""       )
             __lowerCamelCase            =    text_input_ids[:, : self.tokenizer.model_max_length]
         __lowerCamelCase            =    self.text_encoder(text_input_ids.to(self.device       )       )[0]

         # NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
         # While CLIP does normalize the pooled output of the text transformer when combining
         # the image and text embeddings, CLIP does not directly normalize the last hidden state.
         #
         # CLIP normalizing the pooled output.
         # https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
         __lowerCamelCase            =    prompt_embeds / prompt_embeds.norm(dim=-1    ,							keepdim=a       )

         # duplicate text embeddings for each generation per prompt
         __lowerCamelCase            =    prompt_embeds.repeat_interleave(a    ,							dim=0       )

         if do_classifier_free_guidance:
             if self.learned_classifier_free_sampling_embeddings.learnable:
                 __lowerCamelCase            =    self.learned_classifier_free_sampling_embeddings.embeddings
                 __lowerCamelCase            =    negative_prompt_embeds.unsqueeze(0       ).repeat(a    ,							1    ,							1       )
             else:
                 __lowerCamelCase            =    [''''''] * batch_size

                 __lowerCamelCase            =    text_input_ids.shape[-1]
                 __lowerCamelCase            =    self.tokenizer(
                     a    ,							padding='''max_length'''    ,							max_length=a    ,							truncation=a    ,							return_tensors='''pt'''    ,							)
                 __lowerCamelCase            =    self.text_encoder(uncond_input.input_ids.to(self.device       )       )[0]
                 # See comment for normalizing text embeddings
                 __lowerCamelCase            =    negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1    ,							keepdim=a       )

             # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
             __lowerCamelCase            =    negative_prompt_embeds.shape[1]
             __lowerCamelCase            =    negative_prompt_embeds.repeat(1    ,							a    ,							1       )
             __lowerCamelCase            =    negative_prompt_embeds.view(batch_size * num_images_per_prompt    ,							a    ,							-1       )

             # For classifier free guidance, we need to do two forward passes.
             # Here we concatenate the unconditional and text embeddings into a single batch
             # to avoid doing two forward passes
             __lowerCamelCase            =    torch.cat([negative_prompt_embeds, prompt_embeds]       )

         return prompt_embeds






     @torch.no_grad()
     def __call__(					self						:						Union[str, Any]    ,							a						:						Union[str, List[str]]    ,							a						:						int = 1_00    ,							a						:						float = 5.0    ,							a						:						float = 1.0    ,							a						:						int = 1    ,							a						:						Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,							a						:						Optional[torch.FloatTensor] = None    ,							a						:						Optional[str] = "pil"    ,							a						:						bool = True    ,							a						:						Optional[Callable[[int, int, torch.FloatTensor], None]] = None    ,							a						:						int = 1    ,							):





         """simple docstring"""



         if isinstance(a    ,							a       ):
             __lowerCamelCase            =    1
         elif isinstance(a    ,							a       ):
             __lowerCamelCase            =    len(a       )
         else:
             raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(a       )}"""       )

         __lowerCamelCase            =    batch_size * num_images_per_prompt

         __lowerCamelCase            =    guidance_scale > 1.0

         __lowerCamelCase            =    self._encode_prompt(a    ,							a    ,							a       )

         if (callback_steps is None) or (
             callback_steps is not None and (not isinstance(a    ,							a       ) or callback_steps <= 0)
         ):
             raise ValueError(
                 f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
                 f""" {type(a       )}."""       )

         # get the initial completely masked latents unless the user supplied it

         __lowerCamelCase            =    (batch_size, self.transformer.num_latent_pixels)
         if latents is None:
             __lowerCamelCase            =    self.transformer.num_vector_embeds - 1
             __lowerCamelCase            =    torch.full(a    ,							a       ).to(self.device       )
         else:
             if latents.shape != latents_shape:
                 raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""       )
             if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
                 raise ValueError(
                     '''Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,'''
                     f""" {self.transformer.num_vector_embeds - 1} (inclusive)."""       )
             __lowerCamelCase            =    latents.to(self.device       )

         # set timesteps
         self.scheduler.set_timesteps(a    ,							device=self.device       )

         __lowerCamelCase            =    self.scheduler.timesteps.to(self.device       )

         __lowerCamelCase            =    latents

         for i, t in enumerate(self.progress_bar(a       )       ):
             # expand the sample if we are doing classifier free guidance
             __lowerCamelCase            =    torch.cat([sample] * 2       ) if do_classifier_free_guidance else sample

             # predict the un-noised image
             # model_output == `log_p_x_0`
             __lowerCamelCase            =    self.transformer(a    ,							encoder_hidden_states=a    ,							timestep=a       ).sample

             if do_classifier_free_guidance:
                 __lowerCamelCase      ,    __lowerCamelCase            =    model_output.chunk(2       )
                 __lowerCamelCase            =    model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
                 model_output -= torch.logsumexp(a    ,							dim=1    ,							keepdim=a       )

             __lowerCamelCase            =    self.truncate(a    ,							a       )

             # remove `log(0)`'s (`-inf`s)
             __lowerCamelCase            =    model_output.clamp(-70       )

             # compute the previous noisy sample x_t -> x_t-1
             __lowerCamelCase            =    self.scheduler.step(a    ,							timestep=a    ,							sample=a    ,							generator=a       ).prev_sample

             # call the callback, if provided
             if callback is not None and i % callback_steps == 0:
                 callback(a    ,							a    ,							a       )

         __lowerCamelCase            =    self.vqvae.config.vq_embed_dim
         __lowerCamelCase            =    (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
         __lowerCamelCase            =    self.vqvae.quantize.get_codebook_entry(a    ,							shape=a       )
         __lowerCamelCase            =    self.vqvae.decode(a    ,							force_not_quantize=a       ).sample

         __lowerCamelCase            =    (image / 2 + 0.5).clamp(0    ,							1       )
         __lowerCamelCase            =    image.cpu().permute(0    ,							2    ,							3    ,							1       ).numpy()

         if output_type == "pil":
             __lowerCamelCase            =    self.numpy_to_pil(a       )

         if not return_dict:
             return (image,)

         return ImagePipelineOutput(images=a       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[str]    ,							a						:						torch.FloatTensor    ,							a						:						float       ):





         """simple docstring"""



         __lowerCamelCase      ,    __lowerCamelCase            =    torch.sort(a    ,							1    ,							descending=a       )
         __lowerCamelCase            =    torch.exp(a       )
         __lowerCamelCase            =    sorted_p_x_0.cumsum(dim=1       ) < truncation_rate

         # Ensure that at least the largest probability is not zeroed out
         __lowerCamelCase            =    torch.full_like(keep_mask[:, 0:1, :]    ,							a       )
         __lowerCamelCase            =    torch.cat((all_true, keep_mask)    ,							dim=1       )
         __lowerCamelCase            =    keep_mask[:, :-1, :]

         __lowerCamelCase            =    keep_mask.gather(1    ,							indices.argsort(1       )       )

         __lowerCamelCase            =    log_p_x_0.clone()

         __lowerCamelCase            =    -torch.inf  # -inf = log(0)

         return rv

'''simple docstring'''




import json
import os
from typing import Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__UpperCAmelCase					=logging.get_logger(__name__)

__UpperCAmelCase					={
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

__UpperCAmelCase					={
    "vocab_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json"},
    "merges_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt"},
}

__UpperCAmelCase					={
    "ctrl": 2_5_6,
}

__UpperCAmelCase					={
    "Pregnancy": 1_6_8_6_2_9,
    "Christianity": 7_6_7_5,
    "Explain": 1_0_6_4_2_3,
    "Fitness": 6_3_4_4_0,
    "Saving": 6_3_1_6_3,
    "Ask": 2_7_1_7_1,
    "Ass": 9_5_9_8_5,
    "Joke": 1_6_3_5_0_9,
    "Questions": 4_5_6_2_2,
    "Thoughts": 4_9_6_0_5,
    "Retail": 5_2_3_4_2,
    "Feminism": 1_6_4_3_3_8,
    "Writing": 1_1_9_9_2,
    "Atheism": 1_9_2_2_6_3,
    "Netflix": 4_8_6_1_6,
    "Computing": 3_9_6_3_9,
    "Opinion": 4_3_2_1_3,
    "Alone": 4_4_9_6_7,
    "Funny": 5_8_9_1_7,
    "Gaming": 4_0_3_5_8,
    "Human": 4_0_8_8,
    "India": 1_3_3_1,
    "Joker": 7_7_1_3_8,
    "Diet": 3_6_2_0_6,
    "Legal": 1_1_8_5_9,
    "Norman": 4_9_3_9,
    "Tip": 7_2_6_8_9,
    "Weight": 5_2_3_4_3,
    "Movies": 4_6_2_7_3,
    "Running": 2_3_4_2_5,
    "Science": 2_0_9_0,
    "Horror": 3_7_7_9_3,
    "Confession": 6_0_5_7_2,
    "Finance": 1_2_2_5_0,
    "Politics": 1_6_3_6_0,
    "Scary": 1_9_1_9_8_5,
    "Support": 1_2_6_5_4,
    "Technologies": 3_2_5_1_6,
    "Teenage": 6_6_1_6_0,
    "Event": 3_2_7_6_9,
    "Learned": 6_7_4_6_0,
    "Notion": 1_8_2_7_7_0,
    "Wikipedia": 3_7_5_8_3,
    "Books": 6_6_6_5,
    "Extract": 7_6_0_5_0,
    "Confessions": 1_0_2_7_0_1,
    "Conspiracy": 7_5_9_3_2,
    "Links": 6_3_6_7_4,
    "Narcissus": 1_5_0_4_2_5,
    "Relationship": 5_4_7_6_6,
    "Relationships": 1_3_4_7_9_6,
    "Reviews": 4_1_6_7_1,
    "News": 4_2_5_6,
    "Translation": 2_6_8_2_0,
    "multilingual": 1_2_8_4_0_6,
}






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       Union[str, Any]:
    __lowerCamelCase            =    set()
    __lowerCamelCase            =    word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)							)
        __lowerCamelCase            =    char

    __lowerCamelCase            =    set(UpperCamelCase__							)
    return pairs




class        a__ (  UpperCAmelCase__					):
     lowerCamelCase    :	str				=VOCAB_FILES_NAMES
     lowerCamelCase    :	Optional[int]				=PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase    :	Tuple				=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase    :	Union[str, Any]				=CONTROL_CODES






     def __init__(					self						:						Optional[Any]    ,							a						:						str    ,							a						:						List[str]    ,							a						:						Optional[int]="<unk>"    ,							**a						:						Any       ):





         """simple docstring"""



         super().__init__(unk_token=a    ,							**a       )

         with open(a    ,							encoding='''utf-8'''       ) as vocab_handle:
             __lowerCamelCase            =    json.load(a       )
         __lowerCamelCase            =    {v: k for k, v in self.encoder.items()}
         with open(a    ,							encoding='''utf-8'''       ) as merges_handle:
             __lowerCamelCase            =    merges_handle.read().split('''\n'''       )[1:-1]
         __lowerCamelCase            =    [tuple(merge.split()       ) for merge in merges]
         __lowerCamelCase            =    dict(zip(a    ,							range(len(a       )       )       )       )
         __lowerCamelCase            =    {}






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[Any]       ):





         """simple docstring"""



         return len(self.encoder       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						int       ):





         """simple docstring"""



         return dict(self.encoder    ,							**self.added_tokens_encoder       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[Any]    ,							a						:						Dict       ):





         """simple docstring"""



         if token in self.cache:
             return self.cache[token]
         __lowerCamelCase            =    tuple(a       )
         __lowerCamelCase            =    tuple(list(word[:-1]       ) + [word[-1] + '''</w>''']       )
         __lowerCamelCase            =    get_pairs(a       )

         if not pairs:
             return token

         while True:
             __lowerCamelCase            =    min(a    ,							key=lambda a       : self.bpe_ranks.get(a    ,							float('''inf'''       )       )       )
             if bigram not in self.bpe_ranks:
                 break
             __lowerCamelCase      ,    __lowerCamelCase            =    bigram
             __lowerCamelCase            =    []
             __lowerCamelCase            =    0
             while i < len(a       ):
                 try:
                     __lowerCamelCase            =    word.index(a    ,							a       )
                 except ValueError:
                     new_word.extend(word[i:]       )
                     break
                 else:
                     new_word.extend(word[i:j]       )
                     __lowerCamelCase            =    j

                 if word[i] == first and i < len(a       ) - 1 and word[i + 1] == second:
                     new_word.append(first + second       )
                     i += 2
                 else:
                     new_word.append(word[i]       )
                     i += 1
             __lowerCamelCase            =    tuple(a       )
             __lowerCamelCase            =    new_word
             if len(a       ) == 1:
                 break
             else:
                 __lowerCamelCase            =    get_pairs(a       )
         __lowerCamelCase            =    '''@@ '''.join(a       )
         __lowerCamelCase            =    word[:-4]
         __lowerCamelCase            =    word
         return word






     def   SCREAMING_SNAKE_CASE__  (					self						:						str    ,							a						:						List[str]       ):





         """simple docstring"""



         __lowerCamelCase            =    []

         __lowerCamelCase            =    re.findall(R'''\S+\n?'''    ,							a       )

         for token in words:
             split_tokens.extend(list(self.bpe(a       ).split(''' '''       )       )       )
         return split_tokens






     def   SCREAMING_SNAKE_CASE__  (					self						:						Any    ,							a						:						Dict       ):





         """simple docstring"""



         return self.encoder.get(a    ,							self.encoder.get(self.unk_token       )       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[Any]    ,							a						:						Optional[Any]       ):





         """simple docstring"""



         return self.decoder.get(a    ,							self.unk_token       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[Any]    ,							a						:						Dict       ):





         """simple docstring"""



         __lowerCamelCase            =    ''' '''.join(a       ).replace('''@@ '''    ,							''''''       ).strip()
         return out_string






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[Any]    ,							a						:						str    ,							a						:						Optional[str] = None       ):





         """simple docstring"""



         if not os.path.isdir(a       ):
             logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""       )
             return
         __lowerCamelCase            =    os.path.join(
             a    ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )
         __lowerCamelCase            =    os.path.join(
             a    ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']       )

         with open(a    ,							'''w'''    ,							encoding='''utf-8'''       ) as f:
             f.write(json.dumps(self.encoder    ,							indent=2    ,							sort_keys=a    ,							ensure_ascii=a       ) + '''\n'''       )

         __lowerCamelCase            =    0
         with open(a    ,							'''w'''    ,							encoding='''utf-8'''       ) as writer:
             writer.write('''#version: 0.2\n'''       )
             for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,							key=lambda a       : kv[1]       ):
                 if index != token_index:
                     logger.warning(
                         f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                         ''' Please check that the tokenizer is not corrupted!'''       )
                     __lowerCamelCase            =    token_index
                 writer.write(''' '''.join(a       ) + '''\n'''       )
                 index += 1

         return vocab_file, merge_file

     # def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
     #     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
     #     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
     #     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
     #     return ''.join(tokens_generated_so_far)
